MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Ensemble learning is a machine learning paradigm where multiple models (often called “weak learners”) are trained to solve the same problem and combined to get better results. The main hypothesis is that when weak models are correctly combined we can obtain more accurate and/or robust models.

Detailed explanation-2: -Ensemble methods involve combining the predictions from multiple models.

Detailed explanation-3: -Ensemble modeling is a process where multiple diverse models are created to predict an outcome, either by using many different modeling algorithms or using different training data sets. The ensemble model then aggregates the prediction of each base model and results in once final prediction for the unseen data.

Detailed explanation-4: -Ensemble learning is the process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem. Ensemble learning is primarily used to improve the (classification, prediction, function approximation, etc.)

Detailed explanation-5: -An ensemble is an art of combining a diverse set of learners (individual models) together to improvise on the stability and predictive power of the model.